Reliability of gamified reinforcement learning in densely sampled longitudinal assessments.
Reinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has li...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-09-01
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| Series: | PLOS Digital Health |
| Online Access: | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000330&type=printable |
| _version_ | 1797688378961952768 |
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| author | Monja P Neuser Anne Kühnel Franziska Kräutlein Vanessa Teckentrup Jennifer Svaldi Nils B Kroemer |
| author_facet | Monja P Neuser Anne Kühnel Franziska Kräutlein Vanessa Teckentrup Jennifer Svaldi Nils B Kroemer |
| author_sort | Monja P Neuser |
| collection | DOAJ |
| description | Reinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has limited the number of measurements and the test-retest reliability of many decision-related parameters is therefore unknown. In this paper, we present an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) of current mental and physiological states for repeated assessment over weeks. In this task, players have to identify the most effective medication by integrating reward values with changing probabilities to win (according to random Gaussian walks). Participants can complete up to 31 runs with 150 trials each. To encourage replay, in-game screens provide feedback on the progress. Using an initial validation sample of 384 players (9729 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show poor to fair intra-class correlations (ICC: 0.22-0.53), indicating substantial within- and between-subject variance. Notably, items assessing the psychological state showed comparable ICCs as reinforcement learning parameters. To conclude, our innovative and openly customizable app framework provides a gamified task that optimizes repeated assessments of reward learning to better quantify intra- and inter-individual differences in value-based decision-making over time. |
| first_indexed | 2024-03-12T01:30:44Z |
| format | Article |
| id | doaj.art-db9b2000c66a4a2e9e0baecc04308466 |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-12T01:30:44Z |
| publishDate | 2023-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-db9b2000c66a4a2e9e0baecc043084662023-09-12T05:32:19ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702023-09-0129e000033010.1371/journal.pdig.0000330Reliability of gamified reinforcement learning in densely sampled longitudinal assessments.Monja P NeuserAnne KühnelFranziska KräutleinVanessa TeckentrupJennifer SvaldiNils B KroemerReinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has limited the number of measurements and the test-retest reliability of many decision-related parameters is therefore unknown. In this paper, we present an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) of current mental and physiological states for repeated assessment over weeks. In this task, players have to identify the most effective medication by integrating reward values with changing probabilities to win (according to random Gaussian walks). Participants can complete up to 31 runs with 150 trials each. To encourage replay, in-game screens provide feedback on the progress. Using an initial validation sample of 384 players (9729 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show poor to fair intra-class correlations (ICC: 0.22-0.53), indicating substantial within- and between-subject variance. Notably, items assessing the psychological state showed comparable ICCs as reinforcement learning parameters. To conclude, our innovative and openly customizable app framework provides a gamified task that optimizes repeated assessments of reward learning to better quantify intra- and inter-individual differences in value-based decision-making over time.https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000330&type=printable |
| spellingShingle | Monja P Neuser Anne Kühnel Franziska Kräutlein Vanessa Teckentrup Jennifer Svaldi Nils B Kroemer Reliability of gamified reinforcement learning in densely sampled longitudinal assessments. PLOS Digital Health |
| title | Reliability of gamified reinforcement learning in densely sampled longitudinal assessments. |
| title_full | Reliability of gamified reinforcement learning in densely sampled longitudinal assessments. |
| title_fullStr | Reliability of gamified reinforcement learning in densely sampled longitudinal assessments. |
| title_full_unstemmed | Reliability of gamified reinforcement learning in densely sampled longitudinal assessments. |
| title_short | Reliability of gamified reinforcement learning in densely sampled longitudinal assessments. |
| title_sort | reliability of gamified reinforcement learning in densely sampled longitudinal assessments |
| url | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000330&type=printable |
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